Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection

• URL: http://arxiv.org/abs/2303.13916v1
• Date: Fri, 24 Mar 2023 11:12:05 GMT
• Title: Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection
• Authors: Junji Otsuka, Masakazu Yoshimura, Takeshi Ohashi
• Abstract summary: We propose a self-supervised reversed ISP method that does not require metadata and paired images. The proposed method converts a RGB image into a RAW-like image taken in the same environment with the same sensor as a reference RAW image. We show that the proposed method is able to learn various reversed ISPs with comparable accuracy to other state-of-the-art supervised methods.
• Score: 1.1602089225841632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unprocessed sensor outputs (RAW images) potentially improve both low-level and high-level computer vision algorithms, but the lack of large-scale RAW image datasets is a barrier to research. Thus, reversed Image Signal Processing (ISP) which converts existing RGB images into RAW images has been studied. However, most existing methods require camera-specific metadata or paired RGB and RAW images to model the conversion, and they are not always available. In addition, there are issues in handling diverse ISPs and recovering global illumination. To tackle these limitations, we propose a self-supervised reversed ISP method that does not require metadata and paired images. The proposed method converts a RGB image into a RAW-like image taken in the same environment with the same sensor as a reference RAW image by dynamically selecting parameters of the reversed ISP pipeline based on the reference RAW image. The parameter selection is trained via pseudo paired data created from unpaired RGB and RAW images. We show that the proposed method is able to learn various reversed ISPs with comparable accuracy to other state-of-the-art supervised methods and convert unknown RGB images from COCO and Flickr1M to target RAW-like images more accurately in terms of pixel distribution. We also demonstrate that our generated RAW images improve performance on real RAW image object detection task.

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